Data-driven fault diagnosis of bogie suspension components with on-board acoustic sensors

Felix Sorribes Palmer, Bernd Luber, Josef Fuchs, Thomas Kern, Martin Rosenberger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a data-driven approach for fault- detection and isolation of bogie suspension components with on-board acoustic sensors. The fault detection technique is based on the acoustic emissions variation due to structural modal coupling changes in the presence of faulty components. A suspensions component failure introduces an imbalance into the system, resulting in dynamics interferences between the motions. These interferences modify the energy introduced into the system as well as its acoustic emissions. The unknown arbitrary track irregularities generate together with a variable train speed a random nonstationary vehicle excitation. Speech recognition techniques were used to generate features that consider this phenomenon. Frequency spectrums were analysed in different operating conditions to design efficient features. The robustness of the methodology was verified with data from two different test measurement campaigns on a test ring, where the influence of the sensor locations for the fault classification process was studied. The proposed methodology achieved good fault classification performance on the investigated use cases, removed dampers and 50% damper degradation on primary and secondary vertical suspension.
1
Original languageEnglish
Title of host publicationPHME 2020
Subtitle of host publication Proceedings of the 5th European Conference of the Prognostics and Health Management Society
EditorsAnibal Bregon, Kamal Medjaher
Pages1-13
ISBN (Electronic)978-1-936263-32-5
Publication statusPublished - 31 Jul 2020
EventFifth European Conference on the Prognostics and Health Management Society 2020 - Virtual conference, Virtuell, Italy
Duration: 27 Jul 202031 Jul 2020
http://phmeurope.org/2020/

Conference

ConferenceFifth European Conference on the Prognostics and Health Management Society 2020
Abbreviated titlePHME20 Conference
CountryItaly
CityVirtuell
Period27/07/2031/07/20
Internet address

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  • Cite this

    Sorribes Palmer, F., Luber, B., Fuchs, J., Kern, T., & Rosenberger, M. (2020). Data-driven fault diagnosis of bogie suspension components with on-board acoustic sensors. In A. Bregon, & K. Medjaher (Eds.), PHME 2020: Proceedings of the 5th European Conference of the Prognostics and Health Management Society (pp. 1-13)